lmhptransformation.py 3.89 KB
Newer Older
Jait Dixit's avatar
Jait Dixit committed
1
2
3
4
5
6
7
8
9
10
11
import numpy as np
from transformation import Transformation
from d2o import distributed_data_object
from nifty.config import dependency_injector as gdi
import nifty.nifty_utilities as utilities
from nifty import HPSpace, LMSpace

hp = gdi.get('healpy')


class LMHPTransformation(Transformation):
Jait Dixit's avatar
Jait Dixit committed
12
    def __init__(self, domain, codomain=None, module=None):
Jait Dixit's avatar
Jait Dixit committed
13
14
15
16
17
18
19
20
21
22
        if gdi.get('healpy') is None:
            raise ImportError(
                "The module libsharp is needed but not available.")

        if self.check_codomain(domain, codomain):
            self.domain = domain
            self.codomain = codomain
        else:
            raise ValueError("ERROR: Incompatible codomain!")

Jait Dixit's avatar
Jait Dixit committed
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
    @staticmethod
    def get_codomain(domain):
        """
            Generates a compatible codomain to which transformations are
            reasonable, i.e.\  a pixelization of the two-sphere.

            Parameters
            ----------
            domain : LMSpace
                Space for which a codomain is to be generated

            Returns
            -------
            codomain : HPSpace
                A compatible codomain.

            References
            ----------
            .. [#] K.M. Gorski et al., 2005, "HEALPix: A Framework for
                   High-Resolution Discretization and Fast Analysis of Data
                   Distributed on the Sphere", *ApJ* 622..759G.
        """
        if domain is None:
            raise ValueError('ERROR: cannot generate codomain for None')

        if not isinstance(domain, LMSpace):
            raise TypeError('ERROR: domain needs to be a LMSpace')

51
        nside = (domain.lmax + 1) // 3
Jait Dixit's avatar
Jait Dixit committed
52
53
        return HPSpace(nside=nside)

Jait Dixit's avatar
Jait Dixit committed
54
55
56
57
58
59
60
61
62
63
    @staticmethod
    def check_codomain(domain, codomain):
        if not isinstance(domain, LMSpace):
            raise TypeError('ERROR: domain is not a LMSpace')

        if codomain is None:
            return False

        if not isinstance(codomain, HPSpace):
            raise TypeError('ERROR: codomain must be a HPSpace.')
64
65
66
        nside = codomain.nside
        lmax = domain.lmax
        mmax = domain.mmax
Jait Dixit's avatar
Jait Dixit committed
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100

        if (lmax != mmax) or (3 * nside - 1 != lmax):
            return False

        return True

    def transform(self, val, axes=None, **kwargs):
        """
        LM -> HP transform method.

        Parameters
        ----------
        val : np.ndarray or distributed_data_object
            The value array which is to be transformed

        axes : None or tuple
            The axes along which the transformation should take place

        """
        if isinstance(val, distributed_data_object):
            temp_val = val.get_full_data()
        else:
            temp_val = val

        return_val = None

        for slice_list in utilities.get_slice_list(temp_val.shape, axes):
            if slice_list == [slice(None, None)]:
                inp = temp_val
            else:
                if return_val is None:
                    return_val = np.empty_like(temp_val)
                inp = temp_val[slice_list]

101
102
103
            nside = self.codomain.nside
            lmax = self.domain.lmax
            mmax = self.domain.mmax
Jait Dixit's avatar
Jait Dixit committed
104
105
106
107
108
109
110
111
112
113
114
115
116

            inp = inp.astype(np.complex128, copy=False)
            inp = hp.alm2map(inp, nside, lmax=lmax, mmax=mmax,
                             pixwin=False, fwhm=0.0, sigma=None,
                             pol=True, inplace=False)

            if slice_list == [slice(None, None)]:
                return_val = inp
            else:
                return_val[slice_list] = inp

        # re-weight if discrete
        if self.codomain.discrete:
Jait Dixit's avatar
Jait Dixit committed
117
            val = self.codomain.weight(val, power=0.5, axes=axes)
Jait Dixit's avatar
Jait Dixit committed
118
119
120
121
122
123
124
125

        if isinstance(val, distributed_data_object):
            new_val = val.copy_empty(dtype=self.codomain.dtype)
            new_val.set_full_data(return_val, copy=False)
        else:
            return_val = return_val.astype(self.codomain.dtype, copy=False)

        return return_val